load pretrained urls for README.md

hubconf.py

@@ -3,15 +3,38 @@ dependencies = ['paddle', 'numpy']
 
 import paddle
 
-from ppcls.modeling.architectures.resnet import ResNet18 as _ResNet18
-from ppcls.modeling.architectures.resnet import ResNet34 as _ResNet34
-from ppcls.modeling.architectures.resnet import ResNet50 as _ResNet50
+from ppcls.modeling.architectures import resnet as _resnet 
 
 
-_checkpoints = {
-    'ResNet18': 'https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNet18_pretrained.pdparams'
-}
+# _checkpoints = {
+#     'ResNet18': 'https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNet18_pretrained.pdparams',
+#     'ResNet34': 'https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNet34_pretrained.pdparams',
+# }
 
+def _load_pretrained_urls():
+    '''Load pretrained model parameters url from README.md
+    '''
+    import re
+    from collections import OrderedDict
+
+    with open('./README.md', 'r') as f:
+        lines = f.readlines()
+        lines = [lin for lin in lines if lin.strip().startswith('|') and 'Download link' in lin]
+    
+    urls = OrderedDict()
+    for lin in lines:
+        try:
+            name = re.findall(r'\|(.*?)\|', lin)[0].strip().replace('<br>', '')
+            url = re.findall(r'\((.*?)\)', lin)[-1].strip()
+            if name in url:
+                urls[name] = url
+        except:
+            pass
+
+    return urls
+
+
+_checkpoints = _load_pretrained_urls()
 
 
 def ResNet18(**kwargs):
@@ -19,8 +42,9 @@ def ResNet18(**kwargs):
     '''
     pretrained = kwargs.pop('pretrained', False)
 
-    model = _ResNet18(**kwargs)
+    model = _resnet.ResNet18(**kwargs)
     if pretrained:
+        assert 'ResNet18' in _checkpoints, 'Not provide `ResNet18` pretrained model.'
         path = paddle.utils.download.get_weights_path_from_url(_checkpoints['ResNet18'])
         model.set_state_dict(paddle.load(path))
 
@@ -31,13 +55,14 @@ def ResNet18(**kwargs):
 def ResNet34(**kwargs):
     '''ResNet34
     '''
-    model = _ResNet34(**kwargs)
+    pretrained = kwargs.pop('pretrained', False)
+
+    model = _resnet.ResNet34(**kwargs)
+    if pretrained:
+        assert 'ResNet34' in _checkpoints, 'Not provide `ResNet34` pretrained model.'
+        path = paddle.utils.download.get_weights_path_from_url(_checkpoints['ResNet34'])
+        model.set_state_dict(paddle.load(path))
+
     return model
 
 
-
-def ResNet50(**kwargs):
-    '''ResNet50
-    '''
-    model = _ResNet50(**kwargs)
-    return model